class_docstr: An LSTM wrapper serving as an interface for ModelV2s that set use_lstm.
code: def get_initial_state(
      self,
  ) -> Union[List[np.ndarray], List[TensorType]]:
      # Place hidden states on same device as model.
      linear = next(self._logits_branch._model.children())
      h = [
          linear.weight.new(1, self.cell_size).zero_().squeeze(0),
          linear.weight.new(1, self.cell_size).zero_().squeeze(0),
      ]
      return h
  
imports: import numpy
  import gymnasium
  from gymnasium.spaces import Discrete, MultiDiscrete
  import tree
  from typing import Dict, List, Union, Tuple
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.misc import SlimFC
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.policy.rnn_sequencing import add_time_dimension
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils.annotations import override, DeveloperAPI
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.spaces.space_utils import get_base_struct_from_space
  from ray.rllib.utils.torch_utils import flatten_inputs_to_1d_tensor, one_hot
  from ray.rllib.utils.typing import ModelConfigDict, TensorType
  from ray.rllib.utils.deprecation import deprecation_warning
  from ray.util.debug import log_once
label_desc: Use image node_id 4 for calling the LSTMWrapper obj's underlying member method code with example usage: obj.get_initial_state() and returns: h